GDP & emissions - degrowthing or decoupling?

Friday fagpackets

One of a series…

What’s all this about de-growth?

<sigh>

Data

Downloaded from the awesome OurWorldInData.

Code

dt_abs <- data.table::fread(here::here("data", "co2-emissions-and-gdp.csv"))
dt_pc <- data.table::fread(here::here("data", "co2-emissions-and-gdp-per-capita.csv"))

Who do we care about?

Code

entities_of_interest <- c("United Kingdom", "United States", "New Zealand", "China")

Absolute GDP & emissions

First we’ll try production emissions.

Code

plotDT <- dt_abs[Entity %in% entities_of_interest]

ggplot2::ggplot(plotDT, aes(y = as.numeric(`GDP, PPP (constant 2017 international $)`)/1000000000, 
                            x = as.numeric(`Annual CO2 emissions`)/1000000,
                            colour = Entity,
                            alpha = Year)) +
  geom_point() +
  labs(x = "Annual CO2 emissions (production-based, gT)",
       y = "GDP $bn (constant 2017 $)")

Figure 1: GDP vs production emissions over time (selected entities).

Next we’ll try consumption emissions.

Note that 2020 consumption-based emissions data is missing so you don’t see the downtick

Code

plotDT <- dt_abs[Entity %in% entities_of_interest]

ggplot2::ggplot(plotDT, aes(y = as.numeric(`GDP, PPP (constant 2017 international $)`)/1000000000, 
                            x = as.numeric(`Annual consumption-based CO2 emissions`)/1000000,
                            colour = Entity,
                            alpha = Year)) +
  geom_point() +
  labs(x = "Annual CO2 emissions (consumption-based, gT)",
       y = "GDP $bn (constant 2017 $)")

Figure 2: GDP vs consumotion emissions over time (selected entities).

Per capita GDP & emissions

Since we’ll be dividing everything pairwise by the same denominator, nothing much should change… but the plots should be much clearer as we’ve removed the affect of population size

First we’ll try production emissions.

Code

plotDT <- dt_pc[Entity %in% entities_of_interest]

ggplot2::ggplot(plotDT, aes(y = as.numeric(`GDP per capita, PPP (constant 2017 international $)`), 
                            x = as.numeric(`Annual CO2 emissions (per capita)`),
                            colour = Entity,
                            alpha = Year)) +
  geom_point() +
  theme(legend.position = "none") +
  labs(x = "Annual CO2 emissions per capita (production-based, T)",
       y = "GDP per capita (constant 2017 $)")

Figure 3: GDP vs production emissions over time (selected entities)

Let’s repeat that for all countries. Hover over the dots to see which is which #YMMV

Code

#plotDT <- dt_pc[Entity %in% entities_of_interest]
plotDT <- dt_pc # for fun

p <- ggplot2::ggplot(plotDT, aes(y = as.numeric(`GDP per capita, PPP (constant 2017 international $)`), 
                            x = as.numeric(`Annual CO2 emissions (per capita)`),
                            colour = Entity,
                            alpha = Year)) +
  geom_point() +
  theme(legend.position = "none") +
  labs(x = "Annual CO2 emissions per capita (production-based, T)",
       y = "GDP per capita (constant 2017 $)")

plotly::ggplotly(p)

Figure 4: GDP vs production emissions over time (all entities)

Next we’ll try consumption emissions.

Note that 2020 consumption-based emissions data is missing so you don’t see the down-tick

Code

plotDT <- dt_pc[Entity %in% entities_of_interest]

ggplot2::ggplot(plotDT, aes(y = as.numeric(`GDP per capita, PPP (constant 2017 international $)`), 
                            x = as.numeric(`Annual consumption-based CO2 emissions (per capita)`),
                            colour = Entity,
                            alpha = Year)) +
  geom_point() +
  labs(x = "Annual CO2 emissions per capita (consumption-based, T)",
       y = "GDP per capita (constant 2017 $)")

Figure 5: GDP vs consumption emissions over time (selected entities).

Let’s repeat that for all countries. Hover over the dots to see which is which #YMMV

Code

#plotDT <- dt_pc[Entity %in% entities_of_interest]
plotDT <- dt_pc # for fun

p <- ggplot2::ggplot(plotDT, aes(y = as.numeric(`GDP per capita, PPP (constant 2017 international $)`), 
                            x = as.numeric(`Annual consumption-based CO2 emissions (per capita)`),
                            colour = Entity,
                            alpha = Year)) +
  geom_point() +
  labs(x = "Annual CO2 emissions per capita (consumption-based, T)",
       y = "GDP per capita (constant 2017 $)")

plotly::ggplotly(p)

Figure 6: GDP vs consumption emissions over time (all entities)

Efficiency

How about looking at efficency gC02e per $ GDP?

Following https://timjackson.org.uk/earth-vs-growth/ …

Code

dt_abs[, `Emissions intensity` := `Annual CO2 emissions`/`GDP, PPP (constant 2017 international $)`*1000*1000]

entities_of_interest <- c("United Kingdom", "United States", "World", "New Zealand", "China")

Code

plotDT <- dt_abs[Entity %in% entities_of_interest]

ggplot2::ggplot(plotDT, aes(Year, `Emissions intensity`, colour = Entity)) +
  geom_line() +
  labs(y = "gC02 per $ GDP",
       colour = "Country",
       caption = "Data: https://ourworldindata.org")

For the UK, let’s say c. 300g/$ in 1995 reducing to 200g/$ in 2010. So 15 years. To go from 200g/$ to zero at the same rate would take approx. 30 years. So net-zero by 2050?

Regardless of whether that aligns with science-based targets (i.e. absolute zero by 2044) … what happens if the economy grows … by say 2-3% per year? … would that force total emissions up (fixed intensity) or result in efficiency gains (lower intensity)?

Code

dt_abs[, `Emissions intensity (consumption-based)` := `Annual consumption-based CO2 emissions`/`GDP, PPP (constant 2017 international $)`*1000*1000]

Code

plotDT <- dt_abs[Entity %in% entities_of_interest]

ggplot2::ggplot(plotDT, aes(Year, `Emissions intensity (consumption-based)`, colour = Entity)) +
  geom_line() +
  labs(y = "gC02 per $ GDP")

Warning: Removed 4 row(s) containing missing values (geom_path).

Whom do we love?

quarto

data.table

ggplot2

here

skimr

Data descriptions

Check

Skim the absolute data:

Code

skimr::skim(dt_abs)

Warning: Couldn't find skimmers for class: integer64; No user-defined `sfl`
provided. Falling back to `character`.

Warning: Couldn't find skimmers for class: integer64; No user-defined `sfl`
provided. Falling back to `character`.

Data summary

Name	dt_abs
Number of rows	7795
Number of columns	8
Key	NULL
_______________________
Column type frequency:
character	4
numeric	4
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
Entity	0	1.00	4	32	0	255	0
Code	0	1.00	0	8	931	225	0
Annual CO2 emissions	479	0.94	1	21	0	6137	0
GDP, PPP (constant 2017 international $)	1626	0.79	1	21	0	6162	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
Year	0	1.00	2005.06	8.930000e+00	1990.00	1997.00	2005.00	2013.00	2.02000e+03	▇▇▇▇▇
Annual consumption-based CO2 emissions	3730	0.52	976326173.39	3.366486e+09	197201.67	10759532.52	59975058.69	290854945.27	3.67025e+10	▇▁▁▁▁
Emissions intensity	2105	0.73	263.56	2.409400e+02	9.15	122.67	196.73	309.26	2.30112e+03	▇▁▁▁▁
Emissions intensity (consumption-based)	4295	0.45	291.92	1.686800e+02	4.19	179.06	251.82	365.36	1.82195e+03	▇▂▁▁▁

Skim the per capita data

Code

skimr::skim(dt_pc)

Data summary

Name	dt_pc
Number of rows	7745
Number of columns	6
Key	NULL
_______________________
Column type frequency:
character	2
numeric	4
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
Entity	0	1	4	32	0	252	0
Code	0	1	0	8	899	224	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
Year	0	1.00	2005.07	8.93	1990.00	1997.00	2005.00	2013.00	2020.00	▇▇▇▇▇
GDP per capita, PPP (constant 2017 international $)	1579	0.80	17879.29	20132.68	436.72	3678.83	10365.78	25832.03	161971.47	▇▂▁▁▁
Annual consumption-based CO2 emissions (per capita)	3680	0.52	6.53	6.92	0.05	1.19	4.30	9.89	57.79	▇▂▁▁▁
Annual CO2 emissions (per capita)	492	0.94	5.11	6.44	0.02	0.77	2.97	7.42	68.72	▇▁▁▁▁

Code

# hidden chunk